Horizontal and Vertical Integration of Health Care Providers: A Framework for Understanding Various Provider Organizational Structures.

INTRODUCTION: Current U.S. policy and payment initiatives aim to encourage health care provider accountability for population health and higher value care, resulting in efforts to integrate providers along the continuum. Providers work together through diverse organizational structures, yet evidence is limited regarding how to best organize the delivery system to achieve higher value care. METHODS: In 2016, we conducted a narrative review of 10 years of literature to identify definitional components of key organizational structures in the United States. A clear accounting of common organizational structures is foundational for understanding the system attributes that are associated with higher value care. RESULTS: We distinguish between structures characterized by the horizontal integration of providers delivering similar services and the vertical integration of providers fulfilling different functions along the care continuum. We characterize these structures in terms of their origins, included providers and services, care management functions, and governance. CONCLUSIONS AND DISCUSSION: Increasingly, U.S. policymakers seek to promote provider integration and coordination. Emerging evidence suggests that organizational structures, composition, and other characteristics influence cost and quality performance. Given current efforts to reform the U.S. delivery system, future research should seek to systematically examine the role of organizational structure in cost and quality outcomes.

Aggregating Claims Data Across Payers: Approaches, Challenges, and Lessons Learned From the Comprehensive Primary Care Initiative.

The Comprehensive Primary Care (CPC) initiative fueled the emergence of new organizational alliances and financial commitments among payers and primary care practices to use data for performance improvement. In most regions of the country, practices received separate confidential feedback reports of claims-based measures from multiple payers, which varied in content and provided an incomplete picture of a practice's patient panel. Over CPC's last few years, participating payers in several regions resisted the tendency to guard data as a proprietary asset, instead working collaboratively to produce aggregated performance feedback for practices. Aggregating claims data across payers is a potential game changer in improving practice performance because doing so potentially makes the data more accessible, comprehensive, and useful. Understanding lessons learned and key challenges can help other initiatives that are aggregating claims or clinical data across payers for primary care practices or other types of providers.

Leveraging Diverse Data Sources to Identify and Describe U.S. Health Care Delivery Systems.

Health care delivery systems are a growing presence in the U.S., yet research is hindered by the lack of universally agreed-upon criteria to denote formal systems. A clearer understanding of how to leverage real-world data sources to empirically identify systems is a necessary first step to such policy-relevant research. We draw from our experience in the Agency for Healthcare Research and Quality's Comparative Health System Performance (CHSP) initiative to assess available data sources to identify and describe systems, including system members (for example, hospitals and physicians) and relationships among the members (for example, hospital ownership of physician groups). We highlight five national data sources that either explicitly track system membership or detail system relationships: (1) American Hospital Association annual survey of hospitals; (2) Healthcare Relational Services Databases; (3) SK&A Healthcare Databases; (4) Provider Enrollment, Chain, and Ownership System; and (5) Internal Revenue Service 990 forms. Each data source has strengths and limitations for identifying and describing systems due to their varied content, linkages across data sources, and data collection methods. In addition, although no single national data source provides a complete picture of U.S. systems and their members, the CHSP initiative will create an early model of how such data can be combined to compensate for their individual limitations. Identifying systems in a way that can be repeated over time and linked to a host of other data sources will support analysis of how different types of organizations deliver health care and, ultimately, comparison of their performance.

Testing the Replicability of a Successful Care Management Program: Results from a Randomized Trial and Likely Explanations for Why Impacts Did Not Replicate.

OBJECTIVES: To test whether a care management program could replicate its success in an earlier trial and determine likely explanations for why it did not. DATA SOURCES/SETTING: Medicare claims and nurse contact data for Medicare fee-for-service beneficiaries with chronic illnesses enrolled in the trial in eastern Pennsylvania (N = 483). STUDY DESIGN: A randomized trial with half of enrollees receiving intensive care management services and half receiving usual care. We developed and tested hypotheses for why impacts declined. DATA EXTRACTION: All outcomes and covariates were derived from claims and the nurse contact data. PRINCIPAL FINDINGS: From 2010 to 2014, the program did not reduce hospitalizations or generate Medicare savings to offset program fees that averaged $260 per beneficiary per month. These estimates are statistically different (p < .05) from the large reductions in hospitalizations and spending in the first trial (2002-2010). The treatment-control differences in the second trial disappeared because the control group's risk-adjusted hospitalization rate improved, not because the treatment group's outcomes worsened. CONCLUSION: Even if demonstrated in a randomized trial, successful results from one test may not replicate in other settings or time periods. Assessing whether gaps in care that the original program filled exist in other settings can help identify where earlier success is likely to replicate.